ctmm (version 0.2.9)

ctmm.fit: Fit a continuous-time movement model

Description

This function is a wrapper around opim and ctmm.loglike to maximize the likelihood function of continuous-time movement models described in Fleming et al (2014) and Fleming et al (2015), given 2D animal tracking data.

Usage

ctmm.fit(data,CTMM=NULL,...)

ctmm(tau=NULL,isotropic=FALSE,...)

Arguments

data

The 2D timeseries data represented as a telemetry object.

CTMM

A ctmm movement-model object containing the initial guesses for tau and optional component isotropic.

...

Arguments passed to optim.

tau

Array of autocorrelation timescales explained below.

isotropic

A Boolean denoting whether or not the animal's covariance is circular or elliptical.

Value

• ctmm.fit returns the maximum likelihood ctmm movement-model object with the following components: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

encoding

UTF-8

Details

The initial ctmm parameter guess can be the output of variogram.fit or the function ctmm(...) with the argument tau explained below and optionally isotropic=TRUE for a distribution that is symmetric in x and y. tau is an ordered array of autocorrelation timescales. If length(tau)==0, then an uncorrelated bi-variate Gaussian model is fit to the data. If length(tau)==1, then an Ornstein-Uhlenbeck (OU) model (Brownian motion restricted to a finite home range) is fit the data, where tau is the position autocorrelation timescale. tau=Inf then yields Brownian motion (BM). If length(tau)==2, then the OUF model (continuous-velocity motion restricted to a finite home range) is fit to the data, where tau[1] is again the position autocorrelation timescale and tau[2] is the velocity autocorrelation timescale. tau[1]=Inf then yields integrated Ornstein-Uhlenbeck (IOU) motion, which is a spatially unrestricted continuous-velocity process. More models will be implemented in the future.

References

C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, and W. F. Fagan. (2014). From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. http://www.jstor.org/discover/10.1086/675504{The American Naturalist, 183(5), E154-E167.} C. H. Fleming and Y. Subasi and J. M. Calabrese. (2015). A maximum-entropy description of animal movement. http://journals.aps.org/pre/abstract/10.1103/PhysRevE.91.032107{Physical Review E, 91, 032107.}

See Also

variogram.fit, summary.ctmm, ctmm.loglike, optim.

Examples

# Load package and data
library(ctmm)
data(buffalo)
cilla <- buffalo[[1]]

# Fit a continuous-velocity model with tau ~ c(10 days,1 hour)
# also see help(variogram.fit)
m2 <- ctmm(tau=c(10*24*60^2,60^2))
M2 <- ctmm.fit(cilla,m2)

# some human-readable information
summary(M2)